{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "63ff7080-1c4e-4f33-be91-053910694a4d",
   "metadata": {},
   "source": [
    "# Evo 2 Mech Interp Example Notebook\n",
    "### 25.06.05\n",
    "\n",
    "Minimal example of extracting features for an *E. coli* genome chunk and plotting to recreate parts of the main and supplementary figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3673cf1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (3.10.6)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (1.3.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (4.59.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (1.4.9)\n",
      "Requirement already satisfied: numpy>=1.23 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (2.3.3)\n",
      "Requirement already satisfied: packaging>=20.0 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (25.0)\n",
      "Requirement already satisfied: pillow>=8 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (11.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (3.2.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in /large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b47c66e8-e439-4adc-999e-f6f533580ba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09/15/25 10:28:42] </span><span style=\"color: #808000; text-decoration-color: #808000\">WARNING </span> transformer_engine.pytorch.attention.dot_product_attention.utils -      <a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/transformer_engine/pytorch/attention/dot_product_attention/backends.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">backends.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/transformer_engine/pytorch/attention/dot_product_attention/backends.py#98\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">98</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         WARNING - Supported flash-attn versions are &gt;= <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.1</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, &lt;= <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.7</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.</span>post1.   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         Found flash-attn <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.8</span>.<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.</span>post2.                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09/15/25 10:28:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m transformer_engine.pytorch.attention.dot_product_attention.utils -      \u001b]8;id=707555;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/transformer_engine/pytorch/attention/dot_product_attention/backends.py\u001b\\\u001b[2mbackends.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=58520;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/transformer_engine/pytorch/attention/dot_product_attention/backends.py#98\u001b\\\u001b[2m98\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                    \u001b[0m         WARNING - Supported flash-attn versions are >= \u001b[1;36m2.1\u001b[0m.\u001b[1;36m1\u001b[0m, <= \u001b[1;36m2.7\u001b[0m.\u001b[1;36m4.\u001b[0mpost1.   \u001b[2m              \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         Found flash-attn \u001b[1;36m2.8\u001b[0m.\u001b[1;36m0.\u001b[0mpost2.                                           \u001b[2m              \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<torch.autograd.grad_mode.set_grad_enabled at 0x7f7132d4a720>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from typing import List, Optional, Callable\n",
    "\n",
    "import torch\n",
    "\n",
    "from Bio import SeqIO\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from huggingface_hub import hf_hub_download\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from evo2 import Evo2\n",
    "\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "torch.manual_seed(42)\n",
    "torch.cuda.manual_seed(42)\n",
    "torch.set_grad_enabled(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d66bcb-da0f-4e13-ad63-b4bd47a9f7bc",
   "metadata": {},
   "source": [
    "## Load SAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccecc878",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelScope:\n",
    "    \"\"\"Class for adding, using, and removing PyTorch hooks with a model.\"\"\"\n",
    "\n",
    "    def __init__(self, model):\n",
    "        self.model = model\n",
    "        self.hooks = {}\n",
    "        self.activations_cache = {}\n",
    "        self.override_store = {}\n",
    "        self._build_module_dict()\n",
    "\n",
    "    \"\"\"Module listing.\"\"\"\n",
    "    def _build_module_dict(self):\n",
    "        \"\"\"Walks the model's module tree and builds a name: module map.\"\"\"\n",
    "        self._module_dict = {}\n",
    "\n",
    "        def recurse(module, prefix=''):\n",
    "            \"\"\"Recursive tree walk to build self._module_dict.\"\"\"\n",
    "            for name, child in module.named_children():\n",
    "                self._module_dict[prefix+name] = child\n",
    "                recurse(child, prefix=prefix+name+'-')\n",
    "\n",
    "        recurse(self.model)  # build the tree\n",
    "\n",
    "    def list_modules(self):\n",
    "        \"\"\"Lists all modules in the module dictionary.\"\"\"\n",
    "        return self._module_dict.keys()\n",
    "    \n",
    "    \"\"\"Generic hook registration\"\"\"\n",
    "    def add_hook(self, hook_fn, module_str, hook_name):\n",
    "        \"\"\"Add a hook_fn to the module given by module_str.\"\"\"\n",
    "        module = self._module_dict[module_str]\n",
    "        hook_handle = module.register_forward_hook(hook_fn)\n",
    "        self.hooks[hook_name] = hook_handle\n",
    "    \n",
    "    \"\"\"Activations caching\"\"\"\n",
    "    def _build_caching_hook(self, module_str):\n",
    "        self.activations_cache[module_str] = []\n",
    "        def hook_fn(model, input, output):\n",
    "            self.activations_cache[module_str].append(output)\n",
    "\n",
    "        return hook_fn\n",
    "\n",
    "    def add_caching_hook(self, module_str):\n",
    "        \"\"\"Adds an activations caching hook at the location in module_str.\"\"\"\n",
    "        hook_fn = self._build_caching_hook(module_str)\n",
    "        self.add_hook(hook_fn, module_str, 'cache-'+module_str)\n",
    "\n",
    "    def clear_cache(self, module_str):\n",
    "        \"\"\"Clears the activations cache corresponding to module_str.\"\"\"\n",
    "        if module_str not in self.activations_cache.keys():\n",
    "            raise KeyError(f'No activations cache for {module_str}.')\n",
    "        \n",
    "        else:\n",
    "            self.activations_cache[module_str] = []\n",
    "\n",
    "    def clear_all_caches(self):\n",
    "        \"\"\"Clear all activation caches.\"\"\"\n",
    "        for module_str in self.activations_cache.keys():\n",
    "            self.clear_cache(module_str)\n",
    "\n",
    "    def remove_cache(self, module_str):\n",
    "        \"\"\"Remove the cache for module_str.\"\"\"\n",
    "        del self.activations_cache[module_str]\n",
    "\n",
    "    def remove_all_caches(self):\n",
    "        \"\"\"Remove all caches.\"\"\"\n",
    "        caches = list(self.activations_cache.keys())\n",
    "        for cache_str in caches:\n",
    "            self.remove_cache(cache_str)\n",
    "\n",
    "    \"\"\"Activation override\"\"\"\n",
    "    def _build_override_hook(self, module_str):\n",
    "        self.override_store[module_str] = None  # won't override when returned\n",
    "        def hook_fn(model, input, output):\n",
    "            return self.override_store[module_str]\n",
    "        \n",
    "        return hook_fn\n",
    "    \n",
    "    def add_override_hook(self, module_str):\n",
    "        \"\"\"Adds hook to overrides output of module_str using override_store\"\"\"\n",
    "        hook_fn = self._build_override_hook(module_str)\n",
    "        self.add_hook(hook_fn, module_str, 'override-'+module_str)\n",
    "\n",
    "    def override(self, module_str, override_tensor):\n",
    "        \"\"\"Sets the override tensor for module_str.\"\"\"\n",
    "        self.override_store[module_str] = override_tensor\n",
    "\n",
    "    def clear_override(self, module_str):\n",
    "        \"\"\"Clear override hook so it won't affect forward pass.\"\"\"\n",
    "        self.override_store[module_str] = None\n",
    "\n",
    "    def clear_all_overrides(self):\n",
    "        \"\"\"Clear all override hooks.\"\"\"\n",
    "        overrides = list(self.override_store.keys())\n",
    "        for override in overrides:\n",
    "            self.clear_override(override)\n",
    "\n",
    "    \"\"\"Hook clearup\"\"\"\n",
    "    def remove_hook(self, hook_name):\n",
    "        \"\"\"Remove a hook with name hook_name from the model.\"\"\"\n",
    "        self.hooks[hook_name].remove()\n",
    "        del self.hooks[hook_name]\n",
    "\n",
    "    def remove_all_hooks(self):\n",
    "        \"\"\"Remove all hooks from the model.\"\"\"\n",
    "        hooks = list(self.hooks.keys())\n",
    "        for hook_name in hooks:\n",
    "            self.remove_hook(hook_name)\n",
    "\n",
    "\n",
    "INTERVENTION_INTERFACE = Callable[[torch.Tensor], torch.Tensor]\n",
    "\n",
    "\n",
    "class ObservableEvo2:\n",
    "    def __init__(self, model_name: str):\n",
    "        self.model_name = model_name\n",
    "        self.evo_model: NucleotideModel = Evo2(model_name)\n",
    "        self.scope = ModelScope(self.evo_model.model)\n",
    "        self.tokenizer = self.evo_model.tokenizer\n",
    "        self.model = self.evo_model.model\n",
    "        self.d_hidden = 4096\n",
    "\n",
    "    @property\n",
    "    def device(self):\n",
    "        return next(self.evo_model.model.parameters()).device\n",
    "        \n",
    "    @property\n",
    "    def dtype(self):\n",
    "        return self.evo_model.dtype\n",
    "\n",
    "    def list_modules(self):\n",
    "        return self.scope.list_modules()\n",
    "\n",
    "    def forward(\n",
    "        self, \n",
    "        toks: torch.Tensor, \n",
    "        cache_activations_at: Optional[List[str]] = None, \n",
    "        interventions: dict[str, INTERVENTION_INTERFACE] = None,\n",
    "    ):\n",
    "        if not interventions:\n",
    "            interventions = {}\n",
    "\n",
    "        if not cache_activations_at:\n",
    "            cache_activations_at = []\n",
    "\n",
    "        output_cache = {}\n",
    "\n",
    "        layers = list(set(list(interventions.keys()) + cache_activations_at))\n",
    "\n",
    "        if layers:\n",
    "            for layer in layers:\n",
    "                def _intervene(model, input, output):\n",
    "                    acts = output[0] if isinstance(output, tuple) else output\n",
    "\n",
    "                    if layer in interventions:\n",
    "                        acts = interventions[layer](acts)\n",
    "                    '''\n",
    "                    if layer in cache_activations_at and output_cache.get(layer, None) is None:\n",
    "                        output_cache[layer] = [acts]\n",
    "                    elif layer in cache_activations_at:\n",
    "                        output_cache[layer].append(acts)\n",
    "                    '''\n",
    "                    if layer in cache_activations_at:\n",
    "                        output_cache[layer] = acts.detach()\n",
    "                    '''\n",
    "                    if len(output) == 2:\n",
    "                        return (acts, output[1])\n",
    "                    else:\n",
    "                        return acts\n",
    "                    '''\n",
    "                    return (acts, output[1]) if isinstance(output, tuple) else acts\n",
    "                \n",
    "                self.scope.add_hook(_intervene, layer, f'intervene-{layer}')\n",
    "\n",
    "        # Run forwards pass\n",
    "        try:\n",
    "            model_outputs = self.model(toks)\n",
    "            #cache = {key: output[0][0] for key, output in self.scope.activations_cache.items()}\n",
    "            cached_activations = {layer: act.clone() for layer, act in output_cache.items()}\n",
    "        finally:\n",
    "            self.scope.remove_all_hooks()\n",
    "            self.scope.clear_all_caches()\n",
    "                                                   \n",
    "        return model_outputs[0], cached_activations #{layer: act.clone().detach() for layer, act in output_cache.items()}\n",
    "\n",
    "    def generate(\n",
    "        self,\n",
    "        prompt_seqs: List[str],\n",
    "        n_tokens: int = 100,\n",
    "        temperature: float = 1.0,\n",
    "        top_k: int = 4,\n",
    "        top_p: float = 1.,\n",
    "        batched: bool = True,\n",
    "        cached_generation: bool = False,\n",
    "        verbose: int = 0,\n",
    "        cache_activations_at: Optional[List[str]] = None, \n",
    "        interventions: dict[str, INTERVENTION_INTERFACE] = None,\n",
    "    ):\n",
    "        #ACTIVATION_SCALING_CONSTANT = 2.742088556289673\n",
    "        if not interventions:\n",
    "            interventions = {}\n",
    "\n",
    "        if not cache_activations_at:\n",
    "            cache_activations_at = []\n",
    "\n",
    "        output_cache = {}\n",
    "\n",
    "        layers = list(set(list(interventions.keys()) + cache_activations_at))\n",
    "\n",
    "        if layers:\n",
    "            for layer in layers:\n",
    "                def _intervene(model, input, output):\n",
    "                    acts = output[0]\n",
    "\n",
    "                    if layer in interventions:\n",
    "                        acts = interventions[layer](acts) # * ACTIVATION_SCALING_CONSTANT) / ACTIVATION_SCALING_CONSTANT\n",
    "\n",
    "                    if layer in cache_activations_at and output_cache.get(layer, None) is None:\n",
    "                        output_cache[layer] = [acts] # * ACTIVATION_SCALING_CONSTANT]\n",
    "                    elif layer in cache_activations_at:\n",
    "                        output_cache[layer].append(acts)\n",
    "\n",
    "                    if len(output) == 2:\n",
    "                        return (acts, output[1])\n",
    "                    else: \n",
    "                        return acts\n",
    "                    # return (acts, output[1])\n",
    "                \n",
    "                self.scope.add_hook(_intervene, layer, f'intervene-{layer}')\n",
    "\n",
    "        # Run forwards pass\n",
    "        try:\n",
    "            output = self.evo_model.generate(\n",
    "                prompt_seqs,\n",
    "                n_tokens=n_tokens,\n",
    "                temperature=temperature,\n",
    "                top_k=top_k,\n",
    "                top_p=top_p,\n",
    "                batched=batched,\n",
    "                cached_generation=cached_generation,\n",
    "                verbose=verbose,\n",
    "            )\n",
    "        finally:\n",
    "            self.scope.remove_all_hooks()\n",
    "            self.scope.clear_all_caches()\n",
    "\n",
    "        acts_cache = {layer: torch.cat(acts, dim=1).clone().detach() for layer, acts in output_cache.items()}\n",
    "                       \n",
    "        return ''.join(output[0]), acts_cache\n",
    "\n",
    "class BatchTopKTiedSAE(torch.nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        d_in,\n",
    "        d_hidden,\n",
    "        k,\n",
    "        device,\n",
    "        dtype,\n",
    "        tiebreaker_epsilon: float = 1e-6\n",
    "        ):\n",
    "        super().__init__()\n",
    "        self.d_in = d_in\n",
    "        self.d_hidden = d_hidden\n",
    "        self.k = k\n",
    "        \n",
    "        W_mat = torch.randn((d_in, d_hidden))\n",
    "        W_mat = 0.1 * W_mat / torch.linalg.norm(W_mat, dim=0, ord=2, keepdim=True)\n",
    "        self.W = torch.nn.Parameter(W_mat)\n",
    "        self.b_enc = torch.nn.Parameter(torch.zeros(self.d_hidden))\n",
    "        self.b_dec = torch.nn.Parameter(torch.zeros(self.d_in))\n",
    "        self.device = device\n",
    "        self.dtype = dtype\n",
    "        self.tiebreaker_epsilon = tiebreaker_epsilon\n",
    "        self.tiebreaker = torch.linspace(0, tiebreaker_epsilon, d_hidden)\n",
    "        self.to(self.device, self.dtype)\n",
    "        \n",
    "    def encoder_pre(self, x):\n",
    "        return x @ self.W + self.b_enc\n",
    "\n",
    "    def encode(self, x, tiebreak=False):\n",
    "        f = torch.nn.functional.relu(self.encoder_pre(x))\n",
    "        return self._batch_topk(f, self.k, tiebreak=tiebreak)\n",
    "    \n",
    "    def _batch_topk(self, f, k, tiebreak=False):\n",
    "        from math import prod\n",
    "\n",
    "        if tiebreak:  # break ties in feature order for determinism\n",
    "            f += self.tiebreaker.broadcast_to(f)\n",
    "        *input_shape, _ = f.shape  # handle higher-dim tensors (e.g. from sequence input)\n",
    "        numel = k * prod(input_shape)\n",
    "        f_topk = torch.topk(f.flatten(), numel, dim=-1)\n",
    "        f_topk = torch.zeros_like(f.flatten()).scatter(-1, f_topk.indices, f_topk.values).reshape(f.shape)\n",
    "        return f_topk\n",
    "\n",
    "    def decode(self, f):\n",
    "        return f @ self.W.T + self.b_dec\n",
    "\n",
    "    def forward(self, x):\n",
    "        f = self.encode(x)\n",
    "        return self.decode(f), f\n",
    "\n",
    "def load_topk_sae(\n",
    "    sae_path: str,\n",
    "    d_hidden: int,\n",
    "    device: str,\n",
    "    dtype: torch.dtype,\n",
    "    expansion_factor: int = 16,\n",
    "):\n",
    "    sae_dict = torch.load(sae_path, weights_only=True, map_location=\"cpu\")\n",
    "\n",
    "    new_dict = {}\n",
    "    for key, item in sae_dict.items():\n",
    "        new_dict[key.replace(\"_orig_mod.\", \"\").replace(\"module.\", \"\")] = item\n",
    "\n",
    "    sae_dict = new_dict\n",
    "\n",
    "    cached_sae = BatchTopKTiedSAE(\n",
    "        d_hidden,\n",
    "        d_hidden * expansion_factor,\n",
    "        64, # this is a topk64 sae\n",
    "        device,\n",
    "        dtype,\n",
    "    )\n",
    "    cached_sae.load_state_dict(sae_dict)\n",
    "\n",
    "    return cached_sae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9cdf796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/large_storage/hielab/gbrixi/checkpoints/hf_testing/hub/models--Goodfire--Evo-2-Layer-26-Mixed/snapshots/a02b08a876b112d1c5da172e57a59e2bc76b1d70/sae-layer26-mixed-expansion_8-k_64.pt'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = hf_hub_download(\n",
    "    repo_id=f\"Goodfire/Evo-2-Layer-26-Mixed\",\n",
    "    filename=f\"sae-layer26-mixed-expansion_8-k_64.pt\",\n",
    "    repo_type=\"model\"\n",
    ")\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f05ebca9-1cf4-44e4-8066-52cd1c592a44",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fetching 4 files: 100%|██████████| 4/4 [00:00<00:00, 47798.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found complete file in repo: evo2_7b_262k.pt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09/15/25 10:28:43] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> StripedHyena - INFO - Initializing StripedHyena with config:              <a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">model.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#616\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">616</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'model_name'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'shc-evo2-7b-8k-2T-v2'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'vocab_size'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">512</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hidden_size'</span>:  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4096</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_filters'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4096</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hcl_layer_idxs'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23</span>,    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">27</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hcm_layer_idxs'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">15</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">26</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">29</span><span style=\"font-weight: bold\">]</span>,             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'hcs_layer_idxs'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'attn_layer_idxs'</span>:   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">31</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hcm_filter_length'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">128</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hcl_filter_groups'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4096</span>, <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'hcm_filter_groups'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hcs_filter_groups'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hcs_filter_length'</span>:  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_layers'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'short_filter_length'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'num_attention_heads'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span>, <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'short_filter_bias'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'mlp_init_method'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'torch.nn.init.zeros_'</span>,    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'mlp_output_init_method'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'torch.nn.init.zeros_'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'eps'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-06</span>,           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'state_size'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'rotary_emb_base'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10000</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'rotary_emb_scaling_factor'</span>:  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'use_interpolated_rotary_pos_emb'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'make_vocab_size_divisible_by'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'inner_size_multiple_of'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span>,          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'inner_mlp_size'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11008</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'log_intermediate_values'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'proj_groups'</span>: <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hyena_filter_groups'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'column_split_hyena'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'column_split'</span>: <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'interleave'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'evo2_style_activations'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'model_parallel_size'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'pipe_parallel_size'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'tie_embeddings'</span>:      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'mha_out_proj_bias'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'hyena_out_proj_bias'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'hyena_flip_x1x2'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'qkv_proj_bias'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>,                         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'use_fp8_input_projections'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'max_seqlen'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">262144</span>,                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'max_batch_size'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'final_norm'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'use_flash_attn'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'use_flash_rmsnorm'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'use_flash_depthwise'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'use_flashfft'</span>: <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'use_laughing_hyena'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'inference_mode'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>,               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'tokenizer_type'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'CharLevelTokenizer'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'prefill_style'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'fft'</span>,           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'mlp_activation'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'gelu'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'print_activations'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Loader'</span>: <span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">class</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #008000; text-decoration-color: #008000\">'yaml.loader.FullLoader'</span><span style=\"font-weight: bold\">&gt;}</span>                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09/15/25 10:28:43]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Initializing StripedHyena with config:              \u001b]8;id=425244;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=271331;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#616\u001b\\\u001b[2m616\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                    \u001b[0m         \u001b[1m{\u001b[0m\u001b[32m'model_name'\u001b[0m: \u001b[32m'shc-evo2-7b-8k-2T-v2'\u001b[0m, \u001b[32m'vocab_size'\u001b[0m: \u001b[1;36m512\u001b[0m, \u001b[32m'hidden_size'\u001b[0m:  \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         \u001b[1;36m4096\u001b[0m, \u001b[32m'num_filters'\u001b[0m: \u001b[1;36m4096\u001b[0m, \u001b[32m'hcl_layer_idxs'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1;36m2\u001b[0m, \u001b[1;36m6\u001b[0m, \u001b[1;36m9\u001b[0m, \u001b[1;36m13\u001b[0m, \u001b[1;36m16\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m23\u001b[0m,    \u001b[2m            \u001b[0m\n",
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       "\u001b[2;36m                    \u001b[0m         \u001b[32m'hyena_flip_x1x2'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'qkv_proj_bias'\u001b[0m: \u001b[3;91mFalse\u001b[0m,                         \u001b[2m            \u001b[0m\n",
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       "\u001b[2;36m                    \u001b[0m         \u001b[32m'max_batch_size'\u001b[0m: \u001b[1;36m1\u001b[0m, \u001b[32m'final_norm'\u001b[0m: \u001b[3;92mTrue\u001b[0m, \u001b[32m'use_flash_attn'\u001b[0m: \u001b[3;92mTrue\u001b[0m,          \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         \u001b[32m'use_flash_rmsnorm'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'use_flash_depthwise'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'use_flashfft'\u001b[0m: \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         \u001b[3;91mFalse\u001b[0m, \u001b[32m'use_laughing_hyena'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'inference_mode'\u001b[0m: \u001b[3;92mTrue\u001b[0m,               \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         \u001b[32m'tokenizer_type'\u001b[0m: \u001b[32m'CharLevelTokenizer'\u001b[0m, \u001b[32m'prefill_style'\u001b[0m: \u001b[32m'fft'\u001b[0m,           \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         \u001b[32m'mlp_activation'\u001b[0m: \u001b[32m'gelu'\u001b[0m, \u001b[32m'print_activations'\u001b[0m: \u001b[3;91mFalse\u001b[0m, \u001b[32m'Loader'\u001b[0m: \u001b[1m<\u001b[0m\u001b[1;95mclass\u001b[0m\u001b[39m \u001b[0m   \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         \u001b[32m'yaml.loader.FullLoader'\u001b[0m\u001b[1m>\u001b[0m\u001b[1m}\u001b[0m                                                \u001b[2m            \u001b[0m\n"
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       "\u001b[2;36m                    \u001b[0m         per GPU                                                                   \u001b[2m            \u001b[0m\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Assigned \u001b[33mlayer_idx\u001b[0m=\u001b[1;36m0\u001b[0m to \u001b[33mdevice\u001b[0m=\u001b[32m'cuda:0'\u001b[0m             \u001b]8;id=637230;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=678995;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#660\u001b\\\u001b[2m660\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Parameter count for block \u001b[1;36m0\u001b[0m: \u001b[1;36m202426112\u001b[0m              \u001b]8;id=511639;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=585017;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\u001b\\\u001b[2m661\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Assigned \u001b[33mlayer_idx\u001b[0m=\u001b[1;36m1\u001b[0m to \u001b[33mdevice\u001b[0m=\u001b[32m'cuda:0'\u001b[0m             \u001b]8;id=250195;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=814637;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#660\u001b\\\u001b[2m660\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Parameter count for block \u001b[1;36m12\u001b[0m: \u001b[1;36m202461184\u001b[0m             \u001b]8;id=861659;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=734737;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\u001b\\\u001b[2m661\u001b[0m\u001b]8;;\u001b\\\n"
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      ]
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      ]
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Parameter count for block \u001b[1;36m16\u001b[0m: \u001b[1;36m202559488\u001b[0m             \u001b]8;id=616650;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=655126;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\u001b\\\u001b[2m661\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Assigned \u001b[33mlayer_idx\u001b[0m=\u001b[1;36m17\u001b[0m to \u001b[33mdevice\u001b[0m=\u001b[32m'cuda:0'\u001b[0m            \u001b]8;id=182957;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=406981;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#660\u001b\\\u001b[2m660\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Parameter count for block \u001b[1;36m17\u001b[0m: \u001b[1;36m202387456\u001b[0m             \u001b]8;id=379314;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=829290;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\u001b\\\u001b[2m661\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Assigned \u001b[33mlayer_idx\u001b[0m=\u001b[1;36m18\u001b[0m to \u001b[33mdevice\u001b[0m=\u001b[32m'cuda:0'\u001b[0m            \u001b]8;id=216667;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=707990;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#660\u001b\\\u001b[2m660\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Assigned \u001b[33mlayer_idx\u001b[0m=\u001b[1;36m19\u001b[0m to \u001b[33mdevice\u001b[0m=\u001b[32m'cuda:0'\u001b[0m            \u001b]8;id=135901;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=794808;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#660\u001b\\\u001b[2m660\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Assigned \u001b[33mlayer_idx\u001b[0m=\u001b[1;36m20\u001b[0m to \u001b[33mdevice\u001b[0m=\u001b[32m'cuda:0'\u001b[0m            \u001b]8;id=849941;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=347189;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#660\u001b\\\u001b[2m660\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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      ]
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      ]
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Parameter count for block \u001b[1;36m26\u001b[0m: \u001b[1;36m202461184\u001b[0m             \u001b]8;id=541547;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=54486;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\u001b\\\u001b[2m661\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Assigned \u001b[33mlayer_idx\u001b[0m=\u001b[1;36m27\u001b[0m to \u001b[33mdevice\u001b[0m=\u001b[32m'cuda:0'\u001b[0m            \u001b]8;id=227106;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=837004;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#660\u001b\\\u001b[2m660\u001b[0m\u001b]8;;\u001b\\\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Parameter count for block \u001b[1;36m27\u001b[0m: \u001b[1;36m202559488\u001b[0m             \u001b]8;id=998514;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=319442;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\u001b\\\u001b[2m661\u001b[0m\u001b]8;;\u001b\\\n"
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      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> StripedHyena - INFO - Parameter count for block <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">31</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">202387456</span>             <a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">model.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">661</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Parameter count for block \u001b[1;36m31\u001b[0m: \u001b[1;36m202387456\u001b[0m             \u001b]8;id=149089;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=925530;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#661\u001b\\\u001b[2m661\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
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     "text": [
      "100%|██████████| 32/32 [00:00<00:00, 132.38it/s]\n"
     ]
    },
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> StripedHyena - INFO - Initialized model                                   <a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">model.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#680\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">680</span></a>\n",
       "</pre>\n"
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Initialized model                                   \u001b]8;id=635788;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=640685;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#680\u001b\\\u001b[2m680\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> vortex.model.utils - INFO - Loading                                        <a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py#92\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">92</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080\">/large_storage/hielab/gbrixi/checkpoints/hf_testing/hub/models--arcinstitu</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #800080; text-decoration-color: #800080\">te--evo2_7b_262k/snapshots/27c4f055e8b729a27ab5c3e6c8b9c35367a76fba/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">evo2_7</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">b_262k.pt</span>                                                                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
       "</pre>\n"
      ],
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       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m vortex.model.utils - INFO - Loading                                        \u001b]8;id=543477;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=160832;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py#92\u001b\\\u001b[2m92\u001b[0m\u001b]8;;\u001b\\\n",
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       "\u001b[2;36m                    \u001b[0m         \u001b[95mb_262k.pt\u001b[0m                                                                  \u001b[2m           \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extra keys in state_dict: {'blocks.24.mixer.dense._extra_state', 'blocks.27.mixer.mixer.filter.t', 'blocks.13.mixer.mixer.filter.t', 'blocks.30.mixer.mixer.filter.t', 'blocks.3.mixer.dense._extra_state', 'blocks.17.mixer.dense._extra_state', 'blocks.23.mixer.mixer.filter.t', 'blocks.16.mixer.mixer.filter.t', 'blocks.2.mixer.mixer.filter.t', 'unembed.weight', 'blocks.6.mixer.mixer.filter.t', 'blocks.9.mixer.mixer.filter.t', 'blocks.20.mixer.mixer.filter.t', 'blocks.10.mixer.dense._extra_state', 'blocks.31.mixer.dense._extra_state'}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09/15/25 10:28:45] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> StripedHyena - INFO - Adjusting Wqkv for column split <span style=\"font-weight: bold\">(</span>permuting rows<span style=\"font-weight: bold\">)</span>    <a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">model.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#964\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">964</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09/15/25 10:28:45]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m StripedHyena - INFO - Adjusting Wqkv for column split \u001b[1m(\u001b[0mpermuting rows\u001b[1m)\u001b[0m    \u001b]8;id=9671;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py\u001b\\\u001b[2mmodel.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=991160;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/model.py#964\u001b\\\u001b[2m964\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ObservableEvo2(model_name=\"evo2_7b_262k\")\n",
    "topk_sae = load_topk_sae(\n",
    "    file_path,\n",
    "    d_hidden=model.d_hidden,\n",
    "    device=model.device,\n",
    "    dtype=torch.bfloat16,\n",
    "    expansion_factor=8\n",
    ")\n",
    "SAE_LAYER_NAME = 'blocks-26'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "899b6b8c-89d8-4602-82ad-26099e88d1be",
   "metadata": {},
   "source": [
    "## Compute and visualize some features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be481ec7-441b-46c8-9c2e-6dfd94c6e484",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_feature_ts(sae, seq): # Faster, but might crash\n",
    "    toks = model.tokenizer.tokenize(seq)\n",
    "    toks = torch.tensor(toks, dtype=torch.long).unsqueeze(0).to(model.device)\n",
    "    logits, acts = model.forward(toks, cache_activations_at=[SAE_LAYER_NAME])\n",
    "    feats = sae.encode(acts[SAE_LAYER_NAME][0])\n",
    "    return feats.cpu().detach().float().numpy()\n",
    "\n",
    "def get_feature_ts_via_generate(sae, seq): # Slower, but won't crash\n",
    "    logits, acts = model.generate([seq], n_tokens=1, cached_generation=True, cache_activations_at=[SAE_LAYER_NAME])\n",
    "    feats = sae.encode(acts[SAE_LAYER_NAME][0])\n",
    "    return feats.cpu().detach().float().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bca5f108-7029-40df-a207-9742a429dd13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09/15/25 10:28:48] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> vortex.model.utils - INFO - Fixup applied: Allocating cublas workspace    <a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">utils.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py#174\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">174</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         for <span style=\"color: #808000; text-decoration-color: #808000\">device</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>                                                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09/15/25 10:28:48]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m vortex.model.utils - INFO - Fixup applied: Allocating cublas workspace    \u001b]8;id=370513;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py\u001b\\\u001b[2mutils.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=361569;file:///large_storage/hielab/gbrixi/envs/evophage_test/lib/python3.12/site-packages/vortex/model/utils.py#174\u001b\\\u001b[2m174\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                    \u001b[0m         for \u001b[33mdevice\u001b[0m=\u001b[1;36m0\u001b[0m                                                              \u001b[2m            \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1000, 32768)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's get features for 1 kb of human genome sequence (randomly selected from chr17 GRCh38.p14)\n",
    "example_seq = 'TCTGAAAGGACAGTTTTATTGTAGGTACACATGGCTGCCATTTCAAATGTAACTCACAGCTTGTCCATCAGTCCTTGGAGGTCTTTCTATGAAAGGAGCTTGGTGGCGTCCAAACACCACCCAATGTCCACTTAGAAGTAAGCACCGTGTCTGCCCTGAGCTGACTCCTTTTCCAAGGAAGGGGTTGGATCGCTGAGTGTTTTTCCAGGTGTCTACTTGTTGTTAATTAATAGCAATGACAAAGCAGAAGGTTCATGCGTAGCTCGGCTTTCTGGTATTTGCTGCCCGTTGACCAATGGAAGATAAACCTTTGCCTCAGGTGGCACCACTAGCTGGTTAAGAGGCACTTTGTCCTTTCACCCAGGAGCAAACGCACATCACCTGTGTCCTCATCTGATGGCCCTGGTGTGGGGCACAGTCGTGTTGGCAGGGAGGGAGGTGGGGTTGGTCCCCTTTGTGGGTTTGTTGCGAGGCCGTGTTCCAGCTGTTTCCACAGGGAGCGATTTTCAGCTCCACAGGACACTGCTCCCCAGTTCCTCCTGAGAACAAAAGGGGGCGCTGGGGAGAGGCCACCGTTCTGAGGGCTCACTGTATGTGTTCCAGAATCTCCCCTGCAGACCCCCACTGAGGACGGATCTGAGGAACCGGGCTCTGAAACCTCTGATGCTAAGAGCACTCCAACAGCGGAAGGTGGGCCCCCCTTCAGACGCCCCCTCCATGCCTCCAGCCTGTGCTTAGCCGTGCTTTGAGCCTCCCTCCTGGCTGCATCTGCTGCTCCCCCTGGCTGAGAGATGTGCTCACTCCTTCGGTGCTTTGCAGGACAGCGTGGTGGGAGCTGAGCCTTGCGTCGATGCCTTGCTTGCTGGTGCTGAGTGTGGGCACCTTCATCCCGTGTGTGCTCTGGAGGCAGCCACCCTTGGACAGTCCCGCGCACAGCTCCACAAAGCCCCGCTCCATACGATTGTCCTCCCACACCCCCTTCAAAAGCCCCCTCCTCTCT'\n",
    "feature_ts = get_feature_ts(topk_sae, example_seq)\n",
    "feature_ts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2deab19c-7629-415a-9185-79b4dea6f3a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Next, we plot a few of the features\n",
    "selected_features = [15680, 28339, 1050, 25666]\n",
    "fig, axes = plt.subplots(len(selected_features), 1, figsize = (30, 1*len(selected_features)), sharex = True)\n",
    "for ind, feature in enumerate(selected_features):\n",
    "    axes[ind].plot(feature_ts[:, feature], lw=0.5, label=f\"feature {feature}\", alpha = 0.9)\n",
    "    axes[ind].set_xlim(0, feature_ts.shape[0])\n",
    "    axes[ind].set_ylim([0, 7]) # just to look nice\n",
    "    axes[ind].set_yticks([0, 5])\n",
    "    axes[ind].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bada8e18-0de2-469d-b71b-f9e2a80fa7e5",
   "metadata": {},
   "source": [
    "## Demo from paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9aced4fa-fd58-4621-a834-82aaf96e5561",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_relevant_gb_annotations(records, window_start, window_size, \n",
    "                                valid_features={'CDS', 'gene', 'mobile_element', 'misc_feature', \n",
    "                                              'rRNA', 'tRNA', 'ncRNA', 'Regulatory', 'tmRNA'},\n",
    "                                valid_qualifiers={'gene', 'locus_id', 'product', 'mobile_element_type'}):\n",
    "    \"\"\"\n",
    "    Extract annotations from GenBank records within a specified window.\n",
    "    \n",
    "    Args:\n",
    "        records: List of GenBank records\n",
    "        window_start: Start position of window (int)\n",
    "        window_size: Size of window (int)\n",
    "        valid_features: Set of feature types to include\n",
    "        valid_qualifiers: Set of qualifiers to extract\n",
    "    \n",
    "    Returns:\n",
    "        List of annotations: [start, end, type, qualifiers_dict]\n",
    "    \"\"\"\n",
    "    window_end = window_start + window_size\n",
    "    annotations = []\n",
    "    \n",
    "    for record in records:\n",
    "        for feature in record.features:\n",
    "            # Skip features outside window\n",
    "            if feature.location.end < window_start or feature.location.start > window_end:\n",
    "                continue\n",
    "                \n",
    "            if feature.type in valid_features:\n",
    "                # Calculate relative positions within window\n",
    "                start = max(0, feature.location.start - window_start)\n",
    "                end = min(window_size, feature.location.end - window_start)\n",
    "                \n",
    "                # Extract relevant qualifiers\n",
    "                qualifiers = {q: feature.qualifiers[q] for q in valid_qualifiers \n",
    "                            if q in feature.qualifiers}\n",
    "                \n",
    "                annotations.append([start, end, feature.type, qualifiers])\n",
    "    \n",
    "    return annotations\n",
    "\n",
    "\n",
    "def extract_sequence(genbank_file, start, end, strand=\"forward\"):\n",
    "    \"\"\"\n",
    "    Extract sequence from GenBank file at specific coordinates.\n",
    "    \n",
    "    Args:\n",
    "        genbank_file: Path to GenBank file\n",
    "        start: Start position (1-based indexing)\n",
    "        end: End position (1-based indexing)\n",
    "        strand: \"forward\" or \"complement\"\n",
    "    \n",
    "    Returns:\n",
    "        Extracted sequence as string\n",
    "    \"\"\"\n",
    "    record = SeqIO.read(genbank_file, \"genbank\")\n",
    "    seq = record.seq[start-1:end]  # Convert to 0-based indexing\n",
    "    \n",
    "    if strand.lower() == \"complement\":\n",
    "        seq = seq.reverse_complement()\n",
    "        \n",
    "    return str(seq)\n",
    "\n",
    "# Annotation colors\n",
    "ANNOTATION_COLORS = {\n",
    "    'CDS': 'white',\n",
    "    'gene': 'gray', \n",
    "    'mobile_element': 'green',\n",
    "    'misc_feature': 'yellow',\n",
    "    'rRNA': '#7AC8AC',\n",
    "    'tRNA': '#662D91',\n",
    "    'ncRNA': 'white',\n",
    "    'Regulatory': 'red',\n",
    "    'tmRNA': 'red'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60792ce5-1183-44d4-8d83-591872fd61cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get features and plot over a 100kb chunk of the E. coli str. K-12 substr. MG1655 genome, recreating part of the main and supplementary figures\n",
    "# Download from NCBI: https://www.ncbi.nlm.nih.gov/nuccore/556503834\n",
    "genbank_file_path = './NC_000913.gb'\n",
    "start_pos = 4130000\n",
    "end_pos = 4230000\n",
    "selected_features = [13606, 26069, 30262, 2812, 15680, 11734, 24568, 15481]\n",
    "\n",
    "# Load GenBank and get features\n",
    "records = list(SeqIO.parse(genbank_file_path, \"genbank\"))\n",
    "sequence = extract_sequence(genbank_file_path, start_pos, end_pos)\n",
    "annotations = find_relevant_gb_annotations(records, start_pos, end_pos - start_pos)\n",
    "feature_ts = get_feature_ts(topk_sae, sequence)\n",
    "\n",
    "# Plot selected features with genbank annotations visualized as well\n",
    "fig, axes = plt.subplots(len(selected_features), 1,  figsize=(40, len(selected_features)), sharex=True)\n",
    "for i, feature_id in enumerate(selected_features):\n",
    "    axes[i].plot(feature_ts[:, feature_id], lw=0.5, label=f\"feature {feature_id}\", alpha=0.9)\n",
    "    for start, end, feature_type, _ in annotations:\n",
    "        axes[i].axvspan(start, end, color=ANNOTATION_COLORS.get(feature_type, 'black'), alpha=0.2)\n",
    "    axes[i].set_xlim(0, feature_ts.shape[0])\n",
    "    axes[i].set_yticks([0, 5])\n",
    "    axes[i].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9ba8b97-81c1-43b1-85b8-f4cc507f79b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "evophage_test",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
